Complexity-Aware Symbolic Solvers for Sudoku: Towards Interpretability and Cognitive Alignment in Constraint Problem Solving

Complexity-Aware Symbolic Solvers for Sudoku: Towards Interpretability and Cognitive Alignment in Constraint Problem Solving | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Complexity-Aware Symbolic Solvers for Sudoku: Towards Interpretability and Cognitive Alignment in Constraint Problem Solving Rajan Thangamani, R Pallavi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8354205/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract SAT and Constraint Programming (CP) remain the fastest exact Sudoku solvers, on average taking milliseconds to solve even hard puzzles. They are, however, black-box optimizers and hence lack symbolic interpretability. The present article describes a complementary deterministic approach that explains reasoning with four symbolic solvers: Dynamic Constraint Propagation Backtracking (DCPB), Constraint Density–Guided Search (CDGS), Logical Inference Depth (LID) Solver, and Guessing-Complexity-Optimized Solver (GCOS). Each solver is governed by formally stated complexity indicators Constraint Density Metric (CDM), Logical Inference Depth (LID), Constraint-Graph Tightness (CGT), and Guess Entropy (local/global) such that inference effort is transparently computed. Our contributions are threefold. First, we formalize reproducible symbolic complexity measures that capture inference effort and search ambiguity. Second, we implement four deterministic solvers each balancing runtime against interpretability while producing traceable reasoning traces. Third, we introduce the Solver–Complexity Alignment Index (SCAI), a new measure for the extent to which solver effort aligns with intrinsic puzzle hardness. On the Kaggle Sudoku dataset (1M puzzle–solution pairs), we find that while the CP and SAT baselines are unparalleled for runtime efficiency, symbolic solvers dominate in interpretability, reproducibility, and solver–complexity alignment. Most balanced in terms of runtime and SCAI alignment is the LID solver, while best at compressing entropy is GCOS. The work constitutes a contribution to explainable constraint solving and advances the alignment between solver effort and human intuitions on puzzle hardness. The concepts are generalizable to the broader constraint satisfaction, planning, and scheduling applications where interpretability must be as valuable as speed. Our solvers are tested on the 1 million–puzzle Kaggle Sudoku dataset (1,000,000 puzzle–solution pairs; Bryan Park), with detailed experimentation on a stratified subset (10 Easy, 10 Medium, 10 Hard). Sudoku Constraint Programming SAT baseline symbolic complexity deterministic solvers Solver–Complexity Alignment Index (SCAI) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8354205","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563028359,"identity":"da8fd479-65d9-4bd7-a30c-bb375a9fd8c5","order_by":0,"name":"Rajan 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They are, however, black-box optimizers and hence lack symbolic interpretability. The present article describes a complementary deterministic approach that explains reasoning with four symbolic solvers: Dynamic Constraint Propagation Backtracking (DCPB), Constraint Density\u0026ndash;Guided Search (CDGS), Logical Inference Depth (LID) Solver, and Guessing-Complexity-Optimized Solver (GCOS). Each solver is governed by formally stated complexity indicators Constraint Density Metric (CDM), Logical Inference Depth (LID), Constraint-Graph Tightness (CGT), and Guess Entropy (local/global) such that inference effort is transparently computed. Our contributions are threefold. First, we formalize reproducible symbolic complexity measures that capture inference effort and search ambiguity. Second, we implement four deterministic solvers each balancing runtime against interpretability while producing traceable reasoning traces. 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